Why Data Structure is The New Competitive Advantage

There’s a lot of excitement around AI for executive search. In our recent survey, we found that 40% of executive talent leaders always use AI for research and writing. Roughly 70% of early adopters are leveraging AI notetakers during calls, and more than 90% are using LLMs to do things like scan lengthy public filings and extract executive compensation and company insights, or generate candidate summaries. 

But its potential for uncovering deeper market and talent insights based on your team’s proprietary data is far more promising. 

If you’re an executive search leader, the way you structure your data in your CRM is going to become your competitive advantage in the AI era. 

In this post, we’ll explain why and share how to optimize your data structure now to prepare for AI tools.

CRMs Provide The Foundation For a Holistic View of Talent

Executive talent leaders tell us again and again that their success hinges on being able to demonstrate they understand the broader talent landscape, including target companies, available and passive talent pools, skills, competencies, and compensation benchmarks. 

Customer Relationship Management tools, known as CRMs, give teams the best chance of centralizing their talent data to paint this comprehensive picture. 

Data structure refers to how your team organizes, categorizes, and connects data within your CRM.

As a user, you have some control over this–your decisions about how to format, tag, and organize your talent data as it enters the system directly impacts how efficiently AI tools can retrieve it later. 

The underlying framework of your CRM–how the software was built–is the other component. This piece is important because it dictates what you can store and how you are able to relate data points within your system to one another, which impacts reporting, data retrieval, and portability (how to get your data out or share it with other systems). 

This is the primary benefit of selecting a CRM that is specifically built for the executive search industry, such as Thrive TRM, versus a more generic option. While the latter can still act as a source of truth, it won’t achieve the specificity you need to speed up processes later on. Applicant Tracking Systems (ATS) fall down for executive search for the opposite reason: The software is too specific and only allows users to store data related to running the hiring process. 

Generic CRM (Salesforce)	ATS (Greenhouse)	Executive Talent CRM (Thrive TRM)
Contact + Relationship	Candidate + Stage + GDPR	Contact + Relationship +Candidate + Stage + GDPR + Compensation + Business Development
Contact Information Relationship Owner Relationship History Activity	Contact Information Source Stage Activity GDPR	Contact Information Company Information Relationship Owner Relationship History Connections Source Stage Activity GDPR Compensation Business Development

Understand The Two Types of CRM Data

Whichever system you choose–generic CRM, exec-specific CRM, or ATS–it’s important to understand the two types of data you can store: Structured and unstructured data.  

Structured data maintains a consistent format within the CRM and usually takes the form of predefined fields. Because this information is organized and formatted the same way every time, it is easy for AI tools to search, sort, and analyze this data. 

Structured Data

Type	Pre-Defined Fields	Examples
Contact	Contact Information	First and Last Name Email Phone Number
	Experience	Job Title Start Date Company Name
	Contact Tags	Acquisitions >1B Software Development 100-250M Board Member
Candidate	Candidate Tags	High Interest In Multiple Searches References Checked Top 3
	Assessment Rating	5 stars
	Compensation	Desired Salary
Search	Search Stages	Identified Outreach Interview Offer
	Total Candidates	120
	Open and End Dates	March 31, 2024
Company	Company Name	Amazon
	Location	London, UK
	Company Tags	50-100 Employees$250M Revenue
	Industry	FinTech, Healthcare
	Sector	Software, E-Commerce

Unstructured data is the opposite–there is no consistent format, so the data can vary greatly. Most of this data is text-heavy, which makes extracting meaning or patterns more complex.

Unstructured Data

Type	Example
Outreach	Emails InMails
Notes	Interviews Assessments
Documents	Resumes Work Samples

Knowing the difference between these two data types can help you query more effectively and, depending on your goal, add structure where there isn’t any. 

Gain an AI Edge By Structuring Your CRM Data

AI tools respond better to structured data than unstructured data for a few reasons. 

First, the consistent format makes the meaning of each data point clear and easy to understand, which helps with analysis. AI tools can also use standard languages like SQL to index and extract structured data faster. Finally, structured data also allows for better identification of patterns and relationships between different data points. 

On the other hand, unstructured data (text files, notes, images, audio, and videos) all lack a predefined format. Because of this, unstructured data typically needs to be given some sort of structured format before AI tools can be employed to evaluate it.

The more structure you can add to your talent data today, the stronger your position will be to leverage AI applications to query and analyze your data later on.

Strategies to employ within your existing CRM

1. Add Tags

Thrive TRM allows you to create contact, company, and candidate tags to further categorize your data. 

• Contact tags might include specific skills, certifications, off limits status, or M&A events
• Candidate tags could relate to experience, assessments, tenure, or salary
• Company tags could look like industry, stage, revenue, employee count, or location

2. Use the same definitions

When applying tags, create a dictionary for your team that provides the definition for each. This prevents the creation of needless or repetitive tags that will mess with data analysis later. The more uniform you can make your CRM data, the easier it is for AI to understand.

3. Standardize fields using dropdown menus 

If available, configure your CRM with dropdown menus that constrain the available options to users. Sectors and job functions are great examples within Thrive TRM. When adding a new contact to your database, choosing from five function options is easier than creating your own. Again, this encourages data uniformity, which is helpful for indexing. 

3. Require mandatory fields for data consistency

Create procedures for your team to follow that indicate when certain data fields must be filled in. When adding a new company, your rule might be that the company headquarters City and Country must be filled in. Familiarize your team with these practices and review your data quality regularly to ensure compliance. 

Need help structuring your data? Talk with our team about new automations available today.

Use Cases for AI in Executive Talent 

Now that we’ve covered the importance of structuring data within your CRM, here are some examples of how AI can be applied to analyze your talent data. 

1. Find me CMOs in San Francisco with at least 10 years of experience in SaaS companies, strong expertise in customer acquisition and retention, and who have led teams of 15 or more people
2. What is the predicted time-to-fill for open CFO roles in the FinTech industry based on our past performance?
3. Show me a dashboard of our referral outcomes by industry for the last quarter
4. What compensation benchmarks apply to the open CIO role based in Austin, TX? 
5. Using the attached criteria, how effective was the executive performance for placed candidate at RevolutionAI over the past three years?
6. What is the likelihood that this candidate is open to new opportunities?
7. What are the top 5 most in-demand skills for VP of Sales roles in the technology sector in the past year, based on our open and filled positions?
   

These examples may feel far-fetched today, but AI tools and user skills are rapidly proliferating. This will be the reality in the not-too-distant future, so we need to prepare today. 

Conclusion

In order to use AI to query, index, and analyze your talent data and proprietary intelligence faster, the data structure within your CRM must be set up to do so. Adding structure to unstructured data and working with your team to ensure data uniformity are efforts that will pay off as AI continues to evolve for executive search.